One of the main components of spoken language understanding is intent
detection, which allows user goals to be identified. A challenging sub-task of
intent detection is the identification of intent bearing phrases from a limited
amount of training data, while maintaining the ability to generalize well. We
present a new probabilistic topic model for jointly identifying semantic intents
and common phrases in spoken language utterances. Our model jointly learns a set
of intent dependent phrases and captures semantic intent clusters as
distributions over these phrases based on a distance dependent sampling method.
This sampling method uses proximity of words utterances when assigning words to
latent topics. We evaluate our method on labeled utterances and present several
examples of discovered semantic units. We demonstrate that our model outperforms
standard topic models based on bag-of-words assumption.